Method and system for segmentation of medical images

ABSTRACT

A method and system for segmenting three-dimensional (3D) medical images containing an object of interest are provided. The method comprises generating a plurality of successive layers of fixed radius spheres about a circumference of a sphere containing at least one seed point placed within the object of interest when a plurality of respective voxels contained within the spheres exceed a selected threshold. The generation of the layers is repeated until no further voxels contained within an outer surface of each respective layer exceed the selected threshold or a stop seed point is encountered. The layers form a segmented representation of the object of interest.

BACKGROUND OF INVENTION

[0001] This invention relates to segmentation of medical images. Moreparticularly, the invention relates to a method and system forsegmenting an object of interest in three-dimensional medical images foruse in volumetric measurement.

[0002] It is well-known to obtain three-dimensional (3D) arrays of datarepresenting one or more physical properties within an interior of asolid body, for example, anatomical structures. In medical imaging, suchdata is obtained by a variety of non-invasive methods such as computedtomography (CT), magnetic resonance imaging (MRI), ultrasound, positronemission tomography (PET), x-ray or a combination thereof. Regardless ofthe image data acquisition method, the 3D array of data typicallyconsists of a plurality of sets of three-dimensional coordinatesdistributed at regular positions about the body of interest. There are avariety of techniques available to generate a three-dimensional model orstructure. Typically, a seed voxel (volume element) is placed within theanatomical structure of interest and adjacent voxels are successivelyanalyzed and identified as belonging to the same structure generally ifthey are adjacent to a previously identified voxel and they meet aspecified attribute, such as intensity or radiological density. Inaccordance with any of the known techniques, a 3D image is obtained forvisualization.

[0003] The three-dimensional (3D) visualization of internal anatomicalstructures is a known and particularly useful technique for medicalprofessionals and research scientists. Three-dimensional models enablethe ability to rotate the model or virtual representation of theanatomical structure, as well as adjust a point of perspective and zoomin/out from features of interest. Additionally, volumetric measurementsare enabled by a variety of known 3D image processing techniques.

[0004] Three-dimensional visualization and volume measurement is ofparticular interest for studying degenerative brain diseases such asAlzheimer's disease (AD). There are 4 million people in the UnitedStates diagnosed with dementia in Alzheimer's disease. Examination ofthe Alzheimer brain pathology shows extensive β-amyloid plaque, neurontangles and brain atrophy. Typically, magnetic resonance imaging brainvolume measurements are used to monitor the disease progression. Normalaging brain atrophy is only about a 3.5% decrease per decade, but therate of atrophy increases in subjects exhibiting dementia. Thus, brainvolume measurements provide a measurable correlation available topredict Alzheimer's disease.

[0005] Measurements of brain volume from 3D magnetic resonance imageseither by registration methods or by segmentation methods are typicallytedious because manual editing is required to remove the scalp from theintracranial volume in a 3D representation. Supervised segmentationmethods are not sufficiently accurate because of inter observer error.Another technique, known as active contours, has been able to segmentthe brain using a model where the surface of the active contour (bubble)moves at a velocity that depends on curvature and diffusive flow. Thisinvolves growing a bubble constrained by image parameters such asgradients and curvature and constructing a force that stops the bubblegrowth. However, most of the available techniques encounter some degreeof error. For example, the connected volume after segmentation mayinclude regions that are not of interest thus requiring some userintervention. Further, the connected volume may include connectionthrough a undesired narrow region, bridge or other small structure thatconnects different regions that are desirably separated.

[0006] What is needed is a method and system for segmentingthree-dimensional medical images in an automatic manner with minimaluser intervention.

SUMMARY OF INVENTION

[0007] In a first aspect, a method for segmenting three-dimensional (3D)medical images containing an object of interest is provided. The methodcomprises generating a plurality of successive layers of fixed radiusspheres about a circumference of a sphere containing at least one seedpoint placed within the object of interest when a plurality ofrespective voxels contained within the spheres exceed a selectedthreshold. The generation of the layers is repeated until no furthervoxels contained within an outer surface of each respective layer exceedthe selected threshold. The layers form a segmented representation ofthe object of interest.

[0008] In a second aspect, an alternative method for segmentingthree-dimensional (3D) medical images containing an object of interest.The method comprises placing at least one start seed point within theobject of interest and placing at least one stop seed point outside theobject of interest. Successive layers of spheres are generated about acircumference of a sphere containing at the least one start seed pointwhen a plurality of respective voxels contained within the spheresexceed an selected initial threshold. The generation of the layers isrepeated until no further voxels contained within an outer surface ofeach respective layer exceed the selected initial threshold or until theat least one stop seed is encountered to form a segmented representationof the object of interest. The selected threshold is adjusted inresponse to encountering the stop seed point.

[0009] In a third aspect, a system for segmenting medical imagesacquired by an image acquisition device is provided. The systemcomprises a processor coupled to the image acquisition device andadapted to perform segmentation computations in accordance with themethods above and an interface unit coupled to the processor adapted topresent information relating to the segmented representation.

BRIEF DESCRIPTION OF DRAWINGS

[0010] The features and advantages of the present invention will becomeapparent from the following detailed description of the invention whenread with the accompanying drawings in which:

[0011]FIG. 1 is a block diagram illustration of a medical imaging systemfor which embodiments of the present invention are applicable;

[0012] FIGS. 2-5 are illustrations of the processing using embodimentsof the present invention; and

[0013]FIG. 6 is a flow diagram illustration of a process flow of asegmentation method to which embodiments of the present invention areapplicable.

DETAILED DESCRIPTION

[0014] Referring to FIG. 1, a general block diagram of a system 100 fordisease detection is shown. System 100 includes an imaging device 110,which can be selected from a number of medical imaging devices known inthe art for generating a plurality of images. Most commonly, computedtomography (CT) and magnetic resonance imaging (MRI) systems are used togenerate a plurality of medical images.

[0015] During a CT imaging session, a patient lies horizontal and isexposed to a plurality of x-rays measured with a series of X-raydetectors. A beam of x-rays passes through a particular thincross-section or “slice” of the patient. The detectors measure theamount of transmitted radiation. This information is used to compute thex-ray attention coefficient for sample points in the body. A gray scaleimage is then constructed based upon the calculated x-ray attenuationcoefficients. The shades of gray in the image contrast the amount ofx-ray absorption of every point within the slice. The slices obtainedduring a CT session can be reconstructed to provide an anatomicallycorrect representation of the area of interest within the body that hasbeen exposed to the x-rays.

[0016] During a MR imaging session, the patient is placed inside astrong magnetic field generated by a large magnet. Magnetized protonswithin the patient, such as hydrogen atoms, align with the magneticfield produced by the magnet. A particular slice of the patient isexposed to radio waves that create an oscillating magnetic fieldperpendicular to the main magnetic field. The slices can be taken in anyplane chosen by the physician or technician (hereinafter the “operator”)performing the imaging session. The protons in the patient's body firstabsorb the radio waves and then emit the waves by moving out ofalignment with the field. As the protons return to their original state(before excitation), diagnostic images based upon the waves emitted bythe patient's body are created. Like CT image slices, MR image slicescan be reconstructed to provide an overall picture of the body area ofinterest. Parts of the body that produce a high signal are displayed aswhite in an MR image, while those with the lowest signals are displayedas black. Other body parts that have varying signal intensities betweenhigh and low are displayed as some shade of gray.

[0017] Once initial MR or CT images have been obtained, the images aregenerally segmented. The segmentation process classifies the pixels orvoxels of an image into a certain number of classes that are homogeneouswith respect to some characteristic (i.e. intensity, texture, etc.). Forexample, in a segmented image of the brain, the material of the braincan be categorized into three classes: gray matter, white matter, andcerebrospinal fluid. Individual colors can be used to mark regions ofeach class after the segmentation has been completed. Once the segmentedimage is developed, surgeons can use the segmented images to plansurgical techniques.

[0018] Generally, creating a segmented CT or MR image involves severalsteps. A data set is created by capturing CT or MR slices of data.Through the segmentation process, a gray scale value is then assigned toeach point in the data set and different types of tissues will havedifferent gray scale values. Each type of material in the data isassigned a specific value and, therefore, each occurrence of thatmaterial has the same gray scale value. For example, all occurrences ofbone in a particular image may appear in a particular shade of lightgray. This standard of coloring allows the individual viewing the imageto easily understand the objects being represented in the images.

[0019]FIG. 1 illustrates a medical imaging system 100 to whichembodiments of the invention are applicable. The system includes animaging device 110, a processor 120 and an interface unit 130. Imagingdevice 110 is adapted to generate a plurality of image data sets 140 andis, for example, a computed tomography (CT) or magnetic resonance (MR)scanner. In the context of CT or MR, acquisition of image data isgenerally referred to as “scans”. Processor 120 is configured to performcomputations in accordance with embodiments of the present inventionwhich will be described in greater detail with reference to FIGS. 2-6.Processor 120 is also configured to perform computation and controlfunctions for well-known image processing techniques such asreconstruction, image data memory storage, segmentation and the like.Processor 120 may comprise a central processing unit (CPU) such as asingle integrated circuit, such as a microprocessor, or may comprise anysuitable number of integrated circuit devices and/or circuit boardsworking in cooperation to accomplish the functions of a centralprocessing unit. Processor 120 desirably includes memory. Memory withinprocessor 120 may comprise any type of memory known to those skilled inthe art. This includes Dynamic Random Access Memory (DRAM), Static RAM(SRAM), flash memory, cache memory, etc. While not explicitly shown inFIG. 1, the memory may be a single type of memory component or may becomposed of many different types of memory components. Processor 120 isalso capable of executing the programs contained in memory and acting inresponse to those programs or other activities that may occur in thecourse of image acquisition and image viewing. As used herein, “adaptedto”, “configured” and the like refer to mechanical or structuralconnections between elements to allow the elements to cooperate toprovide a described effect; these terms also refer to operationcapabilities of electrical elements such as analog or digital computersor application specific devices (such as an application specificintegrated circuit (ASIC)) that are programmed to perform a sequel toprovide an output in response to given input signals.

[0020] Interface unit 130 is coupled to processor 120 and is adapted toallow human users to communicate with system 100. Processor 120 isfurther adapted to perform computations that are transmitted tointerface unit 130 in a coherent manner such that a human user iscapable of interpreting the transmitted information. Transmittedinformation may include images in 2D or 3D, color and gray scale images,and text messages regarding diagnosis and detection information.Interface unit 130 may be a personal computer, an image work station, ahand held image display unit or any convention image display platformgenerally grouped as part of a CT or MRI system. Referring further toFIG. 1, processor 120 is adapted to perform the segmentation methodswhich will be described in greater detail with reference to FIGS. 2-6and in response to placement of seed points from, for example, interfaceunit 130.

[0021] All data gathered from multiple scans of the patient is to beconsidered one data set. Each data set can be broken up into smallerunits, either pixels or voxels. When the data set is two-dimensional,the image is made up of units called pixels. A pixel is a point intwo-dimensional space that can be referenced using two dimensionalcoordinates, usually x and y. Each pixel in an image is surrounded byeight other pixels, the nine pixels forming a three-by-three square.These eight other pixels, which surround the center pixel, areconsidered the eight-connected neighbors of the center pixel. When thedata set is three-dimensional, the image is displayed in units calledvoxels. A voxel is a point in three-dimensional space that can bereferenced using three-dimensional coordinates, usually x, y and z. Eachvoxel is surrounded by twenty-six other voxels. These twenty-six voxelscan be considered the twenty-six connected neighbors of the originalvoxel.

[0022] In a first embodiment, a method for segmenting three-dimensional(3D) medical images containing an object of interest is provided. Themethod comprises generating a plurality of successive layers of fixedradius spheres about a circumference of a sphere containing at least oneseed point placed within the object of interest when a plurality ofrespective voxels contained within the spheres exceed a selectedthreshold. The method further comprises repeating generation of thelayers until no further voxels contained within an outer surface of eachrespective layer exceed the selected threshold, the layers forming asegmented representation of the object of interest. The radius isselected in accordance with a desired radius of curvature of thesegmented representation. Further in this embodiment, the layers ofspheres within the segmented representation of the object is whollycontained within the object of interest.

[0023] In a further embodiment, a method for segmentingthree-dimensional medical images containing an object of interestcomprises placing at least one seed point in the object of interest andgenerating at least one spherical wavelet having a selected radius aboutthe seed point. A plurality of voxels contained with the wavelet areeach compared with a selected threshold. Additional spherical waveletsare generated circumferentially about the wavelet when the plurality ofvoxels exceed the selected threshold. The plurality of sphericalwavelets having an advancing surface about an outside circumference. Thecomparing and generating steps are repeated for each of the plurality ofvoxels contained within the additional spherical wavelets on theadvancing surface to generate layers of spherical wavelets to form asegmented representation (alternatively referred to as connected volume)of the object of interest. The repeating step is ended when no furthervoxels contained within the advancing surface exceed the selectedthreshold.

[0024] In an exemplary embodiment, the images are three-dimensionalbrain images acquired by Magnetic Resonance Imaging (MRI). However, itis to be appreciated by one skilled in the art that thethree-dimensional images may be acquired techniques other than magneticresonance imaging (MRI), for example computed tomography (CT), positronemission tomography (PET), and x-ray systems and for other objects ofinterest, for example veins and arteries. Generally, three-dimensionalmagnetic brain images have been segmented by connectivity; however,there are usually connections between the intracranial volume and thescalp. One path that connects the brain to the scalp is along the opticnerve to the fluid filled eye globes and then the facial tissue. One ormore seeds in placed in the object (in this case is the brain) andwavelets of a fixed spherical radius are formed. As used herein a“wavelet” refers to a data structure representing the voxels containedwithin a sphere. The radius of the wavelet is selected by the user toprevent connectivity along narrow paths. The wavelets are tested andonly those spherical regions completely composed of voxels above acritical threshold are allowed to propagate. The threshold refers to aparameter defining an object of interest and is generally based onintensity values. The threshold is selected to define the object suchthat the voxels within the object of interest are above the thresholdand the remaining voxels are background. The threshold alone isgenerally not sufficient for segmentation because other objects may alsohave an intensity value above the threshold. For example, the brain andscalp have similarly high relative intensity compared to other voxels.The selected radius and prevention of connectivity along narrow pathswill be discussed in greater detail below. The voxels adjacent to theselected seed voxels are candidates for the creation of active seeds. Atthe boundary of the growing bubble there are active spherical waveletswith active seeds at the center. The bubble is composed of the regionswept out by the wavelets. After each iteration, a layer of waveletspropagate in until there are no more active seeds. The union of all thespherical regions (wavelets) that are completely above the thresholddefine the connected volume (bubble). It is to be noted that regionsthat form bridges smaller in dimension than the selected bubble diameterare not included in the connected region. Further, the connected volumedoes not propagate into noisy regions where the voxels above thresholdare randomly selected. An illustration of the processing of the abovemethod is shown schematically in FIGS. 2-5. An object containing tworegions connected by a small bridge. In practice the bridge may be asmall structure that connects different regions that are desired to beseparatedReferring to FIG. 6, there is shown a flow diagram of anembodiment for segmenting a desired region with a given threshold. Atstep 210, the user places one or more seeds in the desired region. Atstep 220, spherical wavelets are constructed at each seed and testedagainst the interior voxels and if they are all above a threshold thisregion is marked and the center of the wavelet becomes an active seed.At step 230, the voxels adjacent to all the active seeds are then usedto construct spherical wavelets, which are tested against the threshold,and those passing the test are marked and become active seeds. Thebubble consists of the union of the wavelets passing the threshold test.Thereafter, at step 240, the surface of the bubble is an active contour,which grows by layers as the process in continued. The process stopswhen there are no more active seeds found.

[0025] Referring further to FIGS. 2-5, an illustration of the processingof the above embodiments is shown, particularly in preventingconnectivity through a narrow bridge. In FIG. 2, a seed 310 is placed inthe object and the spherical region is found to be completely inside theobject 300. In FIG. 3, adjacent seeds are placed and the correspondingspheres 320 are found to lie completely inside object 300. FIG. 4illustrates a successive layer of spheres 330. The connected volume 340of growing layers fills the available space but does not propegate downthe narrow bridge.

[0026] The embodiments described above are well-adapted if the thresholdcan be selected to segment the region of interest without connecting tothe region across a bridge. However, it may be necessary manually huntfor the correct threshold which may prove tedius and problematic.

[0027] In a further embodiment, an automatic threshold was found byselecting stop seeds in the region away from the region of interest. Abinary search was implemented to speed up the process of finding thecritical threshold that is just above the threshold that connects theobject. The binary search method involves having a threshold rangebetween the minimum and maximum intensity. As the first guess, theaverage intensity is selected.

[0028] In this further embodiment, an alternative method for segementing3D images is provided. The method comprises placing at least one startseed point within the object of interest and placing at least one stopseed point outside the object of interest. Thereafter, successive layersof spheres are generated about a circumference of a sphere containing atthe least one start seed point when a plurality of respective voxelscontained within the spheres exceed an initial selected threshold asdescribed above. The generation of the layers is repeated until nofurther voxels contained within an outer surface of each respectivelayer exceed the selected threshold or until the at least one stop seedis encountered to form a segmented representation, or alternativelyconnected volume, of the object of interest and to prevent connecting toundesired areas. The steps of generating a sphere about a seed point andgenerating successive layers of spheres is performed similarly to theembodiments described above. If the bubble wave connects to the stopseeds, the threshold in increased by half of the first guess otherwiseit is decreased by half of the first guess. The process is repeateduntil the step size is equal to the increment of intensity resolution.The critical threshold is one greater than the maximum value thatconnects the seeds to the stop seeds. The radius of the spheres isselected in accordance with a desired radius of curvature of thesegmented representation.

[0029] While the preferred embodiments of the present invention havebeen shown and described herein, it will be obvious that suchembodiments are provided by way of example only. Numerous variations,changes and substitutions will occur to those of skill in the artwithout departing from the invention herein. Accordingly, it is intendedthat the invention be limited only by the spirit and scope of theappended claims.

1. A method for segmenting three-dimensional (3D) medical imagescontaining an object of interest comprising: generating a plurality ofsuccessive layers of fixed radius spheres about a circumference of asphere containing at least one seed point placed within the object ofinterest when a plurality of respective voxels contained within thespheres exceed a selected threshold; and, repeating generation of thelayers until no further voxels contained within an outer surface of eachrespective layer exceed the selected threshold, the layers forming asegmented representation of the object of interest.
 2. The method ofclaim 1 wherein the radius is selected in accordance with a desiredradius of curvature of the segmented representation.
 3. The method ofclaim 1 wherein the layers of spheres within the segmentedrepresentation of the object is wholly contained within the object ofinterest.
 4. The method of claim 1 wherein the three-dimensional imagesare acquired by at least one of magnetic resonance imaging (MRI),computed tomography (CT), positron emission tomography (PET), and x-raysystems.
 5. A method for segmenting three-dimensional medical imagescontaining an object of interest comprising: placing at least one seedpoint in the object of interest; generating at least one sphericalwavelet having a selected radius about the seed point; comparing aplurality of voxels contained with the wavelet with a selectedthreshold; generating a plurality of additional spherical waveletscircumferentially about the wavelet when the plurality of voxels exceedthe selected threshold; the plurality of spherical wavelets having anadvancing surface about an outside circumference; repeating thecomparing and generating steps for each of the plurality of voxelscontained within the additional spherical wavelets on the advancingsurface to generate layers of spherical wavelets to form a segmentedrepresentation of the object of interest, the repeating step ending whenno further voxels contained within the advancing surface exceed theselected threshold.
 6. The method of claim 5 wherein the radius isselected in accordance with a desired radius of curvature of thesegmented representation.
 7. The method of claim 5 wherein thethree-dimensional images are acquired by at least one of magneticresonance imaging (MRI), computed tomography (CT), positron emissiontomography (PET), and x-ray systems.
 8. A method for segmentingthree-dimensional (3D) medical images containing an object of interestcomprising: placing at least one start seed point within the object ofinterest; placing at least one stop seed point outside the object ofinterest; generating successive layers of spheres about a circumferenceof a sphere containing at the least one start seed point when aplurality of respective voxels contained within the spheres exceed anselected initial threshold; repeating generation of the layers until nofurther voxels contained within an outer surface of each respectivelayer exceed the selected initial threshold or until the at least onestop seed is encountered to form a segmented representation of theobject of interest; and, adjusting the selected threshold in response toencountering the stop seed point.
 9. The method of claim 8 wherein thespheres have a selected radius based on desired radius of curvature. 10.The method of claim 8 wherein the three-dimensional images are acquiredby at least one of magnetic resonance imaging (MRI), computed tomography(CT), positron emission tomography (PET), and x-ray systems.
 11. Themethod of claim 8 wherein the adjusting step is performed with a binarysearch and the selected initial threshold is an average of a minimum andmaximum intensity of the object.
 12. A system for segmenting medicalimages acquired by an image acquisition device comprising: a processorcoupled to the image acquisition device, the processor is adapted toperform segmentation computations and the segmentation computationscomprise: generating a plurality of successive layers of fixed radiusspheres about a circumference of a sphere containing at least one seedpoint placed within the object of interest when a plurality ofrespective voxels contained within the spheres exceed a selectedthreshold; the processor being further adapted to repeat generation ofthe layers until no further voxels contained within an outer surface ofeach respective layer exceed the selected threshold, the layers forminga segmented representation of the object of interest; and, an interfaceunit coupled to the processor adapted to present information relating tothe segmented representation.
 13. The system of claim 12 wherein theprocessor is further adapted to adjust the selected threshold inresponse to encountering a stop seed point.
 14. The system of claim 12wherein the interface unit is further adapted to receive seed point datafrom a user of the system.
 15. The system of claim 12 wherein thespheres have a selected radius based on desired radius of curvature. 16.The system of claim 12 wherein the image acquisition device is at leastone of magnetic resonance imaging (MRI), computed tomography (CT),positron emission tomography (PET), and x-ray systems.